Alexa, let's train now! — A systematic review and classification approach to digital and home-based physical training interventions aiming to support healthy cognitive aging
Notice bibliographique
Résumé
BACKGROUND: There is mounting evidence that regular physical activity is an important prerequisite for healthy cognitive aging. Consequently, the finding that almost one-third of the adult population does not reach the recommended level of regular physical activity calls for further public health actions. In this context, digital and home-based physical training interventions might be a promising alternative to center-based intervention programs. Thus, this systematic review aimed to summarize the current state of the literature on the effects of digital and home-based physical training interventions on adult cognitive performance. METHODS: In this pre-registered systematic review (PROSPERO; ID: CRD42022320031), 5 electronic databases (PubMed, Web of Science, PsycInfo, SPORTDiscus, and Cochrane Library) were searched by 2 independent researchers (FH and PT) to identify eligible studies investigating the effects of digital and home-based physical training interventions on cognitive performance in adults. The systematic literature search yielded 8258 records (extra 17 records from other sources), of which 27 controlled trials were considered relevant. Two reviewers (FH and PT) independently extracted data and assessed the risk of bias using a modified version of the Tool for the assEssment of Study qualiTy and reporting in EXercise (TESTEX scale). RESULTS: Of the 27 reviewed studies, 15 reported positive effects on cognitive and motor-cognitive outcomes (i.e., performance improvements in measures of executive functions, working memory, and choice stepping reaction test), and a considerable heterogeneity concerning study-related, population-related, and intervention-related characteristics was noticed. A more detailed analysis suggests that, in particular, interventions using online classes and technology-based exercise devices (i.e., step-based exergames) can improve cognitive performance in healthy older adults. Approximately one-half of the reviewed studies were rated as having a high risk of bias with respect to completion adherence (≤85%) and monitoring of the level of regular physical activity in the control group. CONCLUSION: The current state of evidence concerning the effectiveness of digital and home-based physical training interventions is mixed overall, though there is limited evidence that specific types of digital and home-based physical training interventions (e.g., online classes and step-based exergames) can be an effective strategy for improving cognitive performance in older adults. However, due to the limited number of available studies, future high-quality studies are needed to buttress this assumption empirically and to allow for more solid and nuanced conclusions.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,044 | 0,118 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,002 |
| Méta-épidémiologie (sens large) | 0,017 | 0,013 |
| Bibliométrie | 0,066 | 0,039 |
| Études des sciences et des technologies | 0,003 | 0,003 |
| Communication savante | 0,008 | 0,010 |
| Science ouverte | 0,004 | 0,005 |
| Intégrité de la recherche | 0,004 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».